OpenRaiser/Intern-Atlas huggingface dataset:
Faster Minimum k-cut of a Simple Graph
Venue: arXiv cs.DS 2019
Year: 2019

We consider the (exact, minimum) $k$-cut problem: given a graph and an integer $k$, delete a minimum-weight set of edges so that the remaining graph has at least $k$ connected components. This problem is a natural generalization of the global minimum cut problem, where the goal is to break the graph into $k=2$ pieces. Our main result is a (combinatorial) $k$-cut algorithm on simple graphs that runs in $n^{(1+o(1))k}$ time for any constant $k$, improving upon the previously best $n^{(2ω/3+o(1))k}$ time algorithm of Gupta et al.~[FOCS'18] and the previously best $n^{(1.981+o(1))k}$ time combinatorial algorithm of Gupta et al.~[STOC'19]. For combinatorial algorithms, this algorithm is optimal up to $o(1)$ factors assuming recent hardness conjectures: we show by a straightforward reduction that $k$-cut on even a simple graph is as hard as $(k-1)$-clique, establishing a lower bound of $n^{(1-o(1))k}$ for $k$-cut. This settles, up to lower-order factors, the complexity of $k$-cut on a simple graph for combinatorial algorithms.
Self-Paced Contextual Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Generalization and adaptation of learned skills to novel situations is a core requirement for intelligent autonomous robots. Although contextual reinforcement learning provides a principled framework for learning and generalization of behaviors across related tasks, it generally relies on uninformed sampling of environments from an unknown, uncontrolled context distribution, thus missing the benefits of structured, sequential learning. We introduce a novel relative entropy reinforcement learning algorithm that gives the agent the freedom to control the intermediate task distribution, allowing for its gradual progression towards the target context distribution. Empirical evaluation shows that the proposed curriculum learning scheme drastically improves sample efficiency and enables learning in scenarios with both broad and sharp target context distributions in which classical approaches perform sub-optimally.
FLEXI: A high order discontinuous Galerkin framework for hyperbolic-parabolic conservation laws
Venue: arXiv cs.CE 2019
Year: 2019

High order (HO) schemes are attractive candidates for the numerical solution of multiscale problems occurring in fluid dynamics and related disciplines. Among the HO discretization variants, discontinuous Galerkin schemes offer a collection of advantageous features which have lead to a strong increase in interest in them and related formulations in the last decade. The methods have matured sufficiently to be of practical use for a range of problems, for example in direct numerical and large eddy simulation of turbulence. However, in order to take full advantage of the potential benefits of these methods, all steps in the simulation chain must be designed and executed with HO in mind. Especially in this area, many commercially available closed-source solutions fall short. In this work, we therefor present the FLEXI framework, a HO consistent, open-source simulation tool chain for solving the compressible Navier-Stokes equations in a high performance computing setting. We describe the numerical algorithms and implementation details and give an overview of the features and capabilities of all parts of the framework. Beyond these technical details, we also discuss the important, but often overlooked issues of code stability, reproducibility and user-friendliness. The benefits gained by developing an open-source framework are discussed, with a particular focus on usability for the open-source community. We close with sample applications that demonstrate the wide range of use cases and the expandability of FLEXI and an overview of current and future developments.
Commonsense Knowledge Base Completion with Structural and Semantic Context
Venue: arXiv cs.CL 2019
Year: 2019

Automatic KB completion for commonsense knowledge graphs (e.g., ATOMIC and ConceptNet) poses unique challenges compared to the much studied conventional knowledge bases (e.g., Freebase). Commonsense knowledge graphs use free-form text to represent nodes, resulting in orders of magnitude more nodes compared to conventional KBs (18x more nodes in ATOMIC compared to Freebase (FB15K-237)). Importantly, this implies significantly sparser graph structures - a major challenge for existing KB completion methods that assume densely connected graphs over a relatively smaller set of nodes. In this paper, we present novel KB completion models that can address these challenges by exploiting the structural and semantic context of nodes. Specifically, we investigate two key ideas: (1) learning from local graph structure, using graph convolutional networks and automatic graph densification and (2) transfer learning from pre-trained language models to knowledge graphs for enhanced contextual representation of knowledge. We describe our method to incorporate information from both these sources in a joint model and provide the first empirical results for KB completion on ATOMIC and evaluation with ranking metrics on ConceptNet. Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1.5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency. Further analysis on model predictions shines light on the types of commonsense knowledge that language models capture well.
Multi-Modal Machine Learning for Flood Detection in News, Social Media and Satellite Sequences
Venue: arXiv cs.CV 2019
Year: 2019

In this paper we present our methods for the MediaEval 2019 Mul-timedia Satellite Task, which is aiming to extract complementaryinformation associated with adverse events from Social Media andsatellites. For the first challenge, we propose a framework jointly uti-lizing colour, object and scene-level information to predict whetherthe topic of an article containing an image is a flood event or not.Visual features are combined using early and late fusion techniquesachieving an average F1-score of82.63,82.40,81.40and76.77. Forthe multi-modal flood level estimation, we rely on both visualand textual information achieving an average F1-score of58.48and46.03, respectively. Finally, for the flooding detection in time-based satellite image sequences we used a combination of classicalcomputer-vision and machine learning approaches achieving anaverage F1-score of58.82%
Decentralized Multi-Agent Actor-Critic with Generative Inference
Venue: arXiv cs.MA 2019
Year: 2019

Recent multi-agent actor-critic methods have utilized centralized training with decentralized execution to address the non-stationarity of co-adapting agents. This training paradigm constrains learning to the centralized phase such that only pre-learned policies may be used during the decentralized phase, which performs poorly when agent communications are delayed, noisy, or disrupted. In this work, we propose a new system that can gracefully handle partially-observable information due to communication disruptions during decentralized execution. Our approach augments the multi-agent actor-critic method's centralized training phase with generative modeling so that agents may infer other agents' observations when provided with locally available context. Our method is evaluated on three tasks that require agents to combine local and remote observations communicated by other agents. We evaluate our approach by introducing both partial observability during decentralized execution, and show that decentralized training on inferred observations performs as well or better than existing actor-critic methods.
From DevOps to DevDataOps: Data Management in DevOps processes
Venue: arXiv cs.SE 2019
Year: 2019

DevOps is a quite effective approach for managing software development and operation, as confirmed by plenty of success stories in real applications and case studies. DevOps is now becoming the main-stream solution adopted by the software industry in development, able to reduce the time to market and costs while improving quality and ensuring evolvability and adaptability of the resulting software architecture. Among the aspects to take into account in a DevOps process, data is assuming strategic importance, since it allows to gain insights from the operation directly into the development, the main objective of a DevOps approach. Data can be therefore considered as the fuel of the DevOps process, requiring proper solutions for its management. Based on the amount of data generated, its variety, velocity, variability, value and other relevant features, DevOps data management can be mainly framed into the BigData category. This allows exploiting BigData solutions for the management of DevOps data generated throughout the process, including artefacts, code, documentation, logs and so on. This paper aims at investigating data management in DevOps processes, identifying related issues, challenges and potential solutions taken from the BigData world as well as from new trends adopting and adapting DevOps approaches in data management, i.e. DataOps.
Task-Adaptive Incremental Learning for Intelligent Edge Devices
Venue: arXiv cs.DC 2019
Year: 2019

Convolutional Neural Networks (CNNs) are used for a wide range of image-related tasks such as image classification and object detection. However, a large pre-trained CNN model contains a lot of redundancy considering the task-specific edge applications. Also, the statically pre-trained model could not efficiently handle the dynamic data in the real-world application. The CNN training data and their labels are collected in an incremental manner. To tackle the above two challenges, we proposed TeAM a task-adaptive incremental learning framework for CNNs in intelligent edge devices. Given a pre-trained large model, TeAM can configure it into any specialized model for dedicated edge applications. The specialized model can be quickly fine-tuned with local data to achieve very high accuracy. Also, with our global aggregation and incremental learning scheme, the specialized CNN models can be collaboratively aggregated to an enhanced global model with new training data.
Voice Packet Performance Estimation through Step Network Using OPNET
Venue: arXiv cs.NI 2019
Year: 2019

VoIP transfer voice over networks such as LAN. This technology is growing rapidly due to support of existing network infrastructure at low cost. Various simulations have been done and it is observed that by increasing the VoIP client, packet length and traffic arrival rate the performance of step network affected. In the current work packet dropped, packet received, voice traffic sent and end-to-end delay is estimated for various queuing disciplines like PQ, FIFO and WFQ. It is depicted that queuing disciplines effects the applications performance and utilization of resources.
SesameBERT: Attention for Anywhere
Venue: arXiv cs.CL 2019
Year: 2019

Fine-tuning with pre-trained models has achieved exceptional results for many language tasks. In this study, we focused on one such self-attention network model, namely BERT, which has performed well in terms of stacking layers across diverse language-understanding benchmarks. However, in many downstream tasks, information between layers is ignored by BERT for fine-tuning. In addition, although self-attention networks are well-known for their ability to capture global dependencies, room for improvement remains in terms of emphasizing the importance of local contexts. In light of these advantages and disadvantages, this paper proposes SesameBERT, a generalized fine-tuning method that (1) enables the extraction of global information among all layers through Squeeze and Excitation and (2) enriches local information by capturing neighboring contexts via Gaussian blurring. Furthermore, we demonstrated the effectiveness of our approach in the HANS dataset, which is used to determine whether models have adopted shallow heuristics instead of learning underlying generalizations. The experiments revealed that SesameBERT outperformed BERT with respect to GLUE benchmark and the HANS evaluation set.
Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning
Venue: arXiv cs.CV 2019
Year: 2019

Computer vision techniques enable automated detection of sky pixels in outdoor imagery. In urban climate, sky detection is an important first step in gathering information about urban morphology and sky view factors. However, obtaining accurate results remains challenging and becomes even more complex using imagery captured under a variety of lighting and weather conditions. To address this problem, we present a new sky pixel detection system demonstrated to produce accurate results using a wide range of outdoor imagery types. Images are processed using a selection of mean-shift segmentation, K-means clustering, and Sobel filters to mark sky pixels in the scene. The algorithm for a specific image is chosen by a convolutional neural network, trained with 25,000 images from the Skyfinder data set, reaching 82% accuracy for the top three classes. This selection step allows the sky marking to follow an adaptive process and to use different techniques and parameters to best suit a particular image. An evaluation of fourteen different techniques and parameter sets shows that no single technique can perform with high accuracy across varied Skyfinder and Google Street View data sets. However, by using our adaptive process, large increases in accuracy are observed. The resulting system is shown to perform better than other published techniques.
Dynamic Mode Decomposition based feature for Image Classification
Venue: arXiv cs.CV 2019
Year: 2019

Irrespective of the fact that Machine learning has produced groundbreaking results, it demands an enormous amount of data in order to perform so. Even though data production has been in its all-time high, almost all the data is unlabelled, hence making them unsuitable for training the algorithms. This paper proposes a novel method of extracting the features using Dynamic Mode Decomposition (DMD). The experiment is performed using data samples from Imagenet. The learning is done using SVM-linear, SVM-RBF, Random Kitchen Sink approach (RKS). The results have shown that DMD features with RKS give competing results.
Approaching the Finite Blocklength Capacity within 0.025dB by Short Polar Codes and CRC-Aided Hybrid Decoding
Venue: arXiv cs.IT 2019
Year: 2019

In this letter, we explore the performance limits of short polar codes and find that the maximum likelihood (ML) performance of a simple CRC-polar concatenated scheme can approach the finite blocklength capacity. Then, in order to approach the ML performance with a low average complexity, a CRC-aided hybrid decoding (CA-HD) algorithm is proposed and its decoding process is divided into two steps. In the first step, the received sequence is decoded by the adaptive successive cancellation list (ADSCL) decoding. In the second step, CRC-aided sphere decoding with a reasonable initial radius is used to decode the received sequence. To obtain the reasonable radius, the CRC bits of the survival paths in ADSCL are recalculated and the minimum Euclidean distance between the survival path and the received sequence is chosen as the initial radius. The simulation results show that CA-HD can achieve within about $0.025$dB of the finite blocklength capacity at the block error ratio $10^{-3}$ with code length $128$ and code rate $1/2$.
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages. We combine two existing objective functions to make images and captions close in a joint embedding space while adapting the alignment of word embeddings between existing languages in our model. We show that our approach enables better generalization, achieving state-of-the-art performance in text-to-image and image-to-text retrieval task, and caption-caption similarity task. Two multimodal multilingual datasets are used for evaluation: Multi30k with German and English captions and Microsoft-COCO with English and Japanese captions.
One-To-Many Multilingual End-to-end Speech Translation
Venue: arXiv cs.CL 2019
Year: 2019

Nowadays, training end-to-end neural models for spoken language translation (SLT) still has to confront with extreme data scarcity conditions. The existing SLT parallel corpora are indeed orders of magnitude smaller than those available for the closely related tasks of automatic speech recognition (ASR) and machine translation (MT), which usually comprise tens of millions of instances. To cope with data paucity, in this paper we explore the effectiveness of transfer learning in end-to-end SLT by presenting a multilingual approach to the task. Multilingual solutions are widely studied in MT and usually rely on ``\textit{target forcing}'', in which multilingual parallel data are combined to train a single model by prepending to the input sequences a language token that specifies the target language. However, when tested in speech translation, our experiments show that MT-like \textit{target forcing}, used as is, is not effective in discriminating among the target languages. Thus, we propose a variant that uses target-language embeddings to shift the input representations in different portions of the space according to the language, so to better support the production of output in the desired target language. Our experiments on end-to-end SLT from English into six languages show important improvements when translating into similar languages, especially when these are supported by scarce data. Further improvements are obtained when using English ASR data as an additional language (up to $+2.5$ BLEU points).
Improved lower bounds on parity vertex colourings of binary trees
Venue: arXiv cs.DM 2019
Year: 2019

A vertex colouring is called a \emph{parity vertex colouring} if every path in $G$ contains an odd number of occurrences of some colour. Let $χ_{p}(G)$ be the minimal number of colours in a parity vertex colouring of $G$. We show that $χ_{p}(B^*) \ge \sqrt{d} + \frac{1}{4} \log_2(d) - \frac{1}{2}$ where $B^*$ is a subdivision of the complete binary tree $B_d$. This improves the previously known bound $χ_{p}(B^*) \ge \sqrt{d}$ and enhances the techniques used for proving lower bounds. We use this result to show that $χ_{p}(T) > \sqrt[3]{\log{n}}$ where $T$ is any binary tree with $n$ vertices. These lower bounds are also lower bounds for the conflict-free colouring. We also prove that $χ_{p}(G)$ is not monotone with respect to minors and determine its value for cycles. Furthermore, we study complexity of computing the parity vertex chromatic number $χ_{p}(G)$. We show that checking whether a vertex colouring is a parity vertex colouring is coNP-complete. Then we use Courcelle's theorem to prove that the problem of checking whether $χ_{p}(G) \le k$ is fixed-parameter tractable with respect $k$ and the treewidth of $G$.
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019
Venue: arXiv cs.CL 2019
Year: 2019

Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature. This paper presents our findings from participating in BioNLP Shared Tasks 2019. We addressed Named Entity Recognition including nested entities extraction, Entity Normalization and Relation Extraction. Our proposed approach of Named Entities can be generalized to different languages and we have shown it's effectiveness for English and Spanish text. We investigated linguistic features, hybrid loss including ranking and Conditional Random Fields (CRF), multi-task objective and token-level ensembling strategy to improve NER. We employed dictionary based fuzzy and semantic search to perform Entity Normalization. Finally, our RE system employed Support Vector Machine (SVM) with linguistic features. Our NER submission (team:MIC-CIS) ranked first in BB-2019 norm+NER task with standard error rate (SER) of 0.7159 and showed competitive performance on PharmaCo NER task with F1-score of 0.8662. Our RE system ranked first in the SeeDev-binary Relation Extraction Task with F1-score of 0.3738.
Length-Bounded Cuts: Proper Interval Graphs and Structural Parameters
Venue: arXiv cs.DS 2019
Year: 2019

In the presented paper we study the Length-Bounded Cut problem for special graph classes as well as from a parameterized-complexity viewpoint. Here, we are given a graph $G$, two vertices $s$ and $t$, and positive integers $β$ and $λ$. The task is to find a set of edges $F$ of size at most $β$ such that every $s$-$t$-path of length at most $λ$ in $G$ contains some edge in $F$. Bazgan et al. conjectured that Length-Bounded Cut admits a polynomial-time algorithm if the input graph $G$ is a~proper interval graph. We confirm this conjecture by showing a dynamic-programming based polynomial-time algorithm. We strengthen the W[1]-hardness result of Dvořák and Knop. Our reduction is shorter, seems simpler to describe, and the target of the reduction has stronger structural properties. Consequently, we give W[1]-hardness for the combined parameter pathwidth and maximum degree of the input graph. Finally, we prove that Length-Bounded Cut is W[1]-hard for the feedback vertex number. Both our hardness results complement known XP algorithms.
Refining 6D Object Pose Predictions using Abstract Render-and-Compare
Venue: arXiv cs.CV 2019
Year: 2019

Robotic systems often require precise scene analysis capabilities, especially in unstructured, cluttered situations, as occurring in human-made environments. While current deep-learning based methods yield good estimates of object poses, they often struggle with large amounts of occlusion and do not take inter-object effects into account. Vision as inverse graphics is a promising concept for detailed scene analysis. A key element for this idea is a method for inferring scene parameter updates from the rasterized 2D scene. However, the rasterization process is notoriously difficult to invert, both due to the projection and occlusion process, but also due to secondary effects such as lighting or reflections. We propose to remove the latter from the process by mapping the rasterized image into an abstract feature space learned in a self-supervised way from pixel correspondences. Using only a light-weight inverse rendering module, this allows us to refine 6D object pose estimations in highly cluttered scenes by optimizing a simple pixel-wise difference in the abstract image representation. We evaluate our approach on the challenging YCB-Video dataset, where it yields large improvements and demonstrates a large basin of attraction towards the correct object poses.
ABEONA: an Architecture for Energy-Aware Task Migrations from the Edge to the Cloud
Venue: arXiv cs.DC 2019
Year: 2019

This paper presents our preliminary results with ABEONA, an edge-to-cloud architecture that allows migrating tasks from low-energy, resource-constrained devices on the edge up to the cloud. Our preliminary results on artificial and real world datasets show that it is possible to execute workloads in a more efficient manner energy-wise by scaling horizontally at the edge, without negatively affecting the execution runtime.